With the success in synthesizing realistic faces, it's harder to find visual forgery clues.
But the discrepancy between natural and manipulated faces in the frequency domain, especially in middle and high frequency bands, is pretty apparent as illustrated in Figure REF .
Previous studies mostly use fixed filter banks or hand-crafted methods from other fields to extract frequency information [1]}, [2]}, [3]}, [4]}.
However, considering the diversity of background, gender, age, skin color, and especially manipulation methods, these methods based on incomprehensive prior knowledge are insufficient to capture forgery patterns of frequency.
In order to tackle this problem, inspired by [5]}, [6]}, [7]}, [8]},
we propose an adaptive frequency feature generation module (AFFGM) to efficiently mine subtle artifacts from the frequency domain.
Our AFFGM consists of two parts: data preprocessing and adaptive frequency information mining block.
Next, we will introduce them respectively.
<FIGURE>